





"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = ['pixel_values']





      def __init__(			self ,		lowercase = True ,		lowercase = 32 ,		lowercase=PILImageResampling.BILINEAR ,		lowercase = True ,		**lowercase ,		)						->	None:
            lowerCAmelCase    		=			do_resize
            lowerCAmelCase    		=			do_rescale
            lowerCAmelCase    		=			size_divisor
            lowerCAmelCase    		=			resample
            super().__init__(**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase = None ,		**lowercase					)						->	np.ndarray:
            lowerCAmelCase		,						lowerCAmelCase    		=			get_image_size(lowercase					)
            # Rounds the height and width down to the closest multiple of size_divisor
            lowerCAmelCase    		=			height // size_divisor * size_divisor
            lowerCAmelCase    		=			width // size_divisor * size_divisor
            lowerCAmelCase    		=			resize(lowercase ,		(new_h, new_w) ,		resample=lowercase ,		data_format=lowercase ,		**lowercase					)
            return image





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase = None ,		**lowercase					)						->	np.ndarray:
            return rescale(image=lowercase ,		scale=lowercase ,		data_format=lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		lowercase = None ,		lowercase=None ,		lowercase = None ,		lowercase = None ,		lowercase = ChannelDimension.FIRST ,		**lowercase ,		)						->	BatchFeature:
            lowerCAmelCase    		=			do_resize if do_resize is not None else self.do_resize
            lowerCAmelCase    		=			do_rescale if do_rescale is not None else self.do_rescale
            lowerCAmelCase    		=			size_divisor if size_divisor is not None else self.size_divisor
            lowerCAmelCase    		=			resample if resample is not None else self.resample

            if do_resize and size_divisor is None:
                  raise ValueError("""size_divisor is required for resizing"""					)

            lowerCAmelCase    		=			make_list_of_images(lowercase					)

            if not valid_images(lowercase					):
                  raise ValueError("""Invalid image(s)"""					)

            # All transformations expect numpy arrays.
            lowerCAmelCase    		=			[to_numpy_array(lowercase					) for img in images]

            if do_resize:
                  lowerCAmelCase    		=			[self.resize(lowercase ,		size_divisor=lowercase ,		resample=lowercase					) for image in images]

            if do_rescale:
                  lowerCAmelCase    		=			[self.rescale(lowercase ,		scale=1 / 255					) for image in images]

            lowerCAmelCase    		=			[to_channel_dimension_format(lowercase ,		lowercase					) for image in images]

            lowerCAmelCase    		=			{"""pixel_values""": images}
            return BatchFeature(data=lowercase ,		tensor_type=lowercase					)













"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor

@require_torch
@require_vision
class 						lowercase   (   unittest.TestCase   ):





      @slow
      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            lowerCAmelCase    		=			AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            model.to(lowercase					)

            from datasets import load_dataset

            lowerCAmelCase    		=			load_dataset("""nielsr/rvlcdip-demo"""					)

            lowerCAmelCase    		=			dataset["""train"""][0]["""image"""].convert("""RGB"""					)

            lowerCAmelCase    		=			image_processor(lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)
                  lowerCAmelCase    		=			outputs.logits

            lowerCAmelCase    		=			torch.Size((1, 16)					)
            self.assertEqual(logits.shape ,		lowercase					)

            lowerCAmelCase    		=			torch.tensor(
                [-0.4_158, -0.4_092, -0.4_347] ,		device=lowercase ,		dtype=torch.float ,		)
            self.assertTrue(torch.allclose(logits[0, :3] ,		lowercase ,		atol=1e-4					)					)






